Implementation of a parametrized infinite-horizon model predictive control scheme with stability guarantees

This article discusses the implementation of an infinite-horizon model predictive control approach that is based on representing input and state trajectories by a linear combination of basis functions. An iterative constraint sampling strategy is presented for guaranteeing constraint satisfaction over all times. It will be shown that the proposed method converges. In addition, we will discuss the implementation of the resulting (online) model predictive control algorithm on an unmanned aerial vehicle and provide experimental results. The computational efficiency of the algorithm is highlighted by the fact that a sampling rate of 100 Hz was achieved on an embedded platform.

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